Professor Aberlardo Pardo, The University of Sydney
Wednesday, 13th December, 2017
12:00 - 15:00pm, Room 4.31/4.33, Informatics Forum, University of Edinburgh, 10 Crichton Street, Edinburgh
The amount of data extracted from learning experiences has grown at an astonishing pace both in depth due to the increasing variety of data sources, and in breath with courses now being offered to massive student cohorts. However, in this emerging scenario instructors are now facing the challenge of connecting the knowledge emerging from data analysis with the provision of meaningful support actions to students within the context of an instructional design.
The three-hour workshop comprises of a set of hands-on activities with prepared material. Attendees are required to bring their own laptop. The nature of the topic will appeal to researchers and practitioners in the areas of learning analytics, educational data mining, instructional design, and any academic interested in improving the timeliness and relevance of feedback and support for their students.
To guarantee a productive session, attendance will be limited to 30 people.
The objectives of the tutorial are to:
Use exploratory data analysis to summarize data sets derived from a learning experience.
Identify student support actions to be deployed while the experience is being delivered.
Define a set of low latency actions (those reaching students within a day) that are personalized to each student.
Express the connection between data and actions in a formalism suitable to be deployed at scale.
Apply the OnTask tool to provide personalized feedback at scale.
The emphasis of this session is not on using specific data-mining methods, but on articulating the connection between the knowledge extracted from those tools and specific actions to support students.
OnTask is a software tool that gathers and assesses data about students’ activities throughout the semester and allows instructors to design personalised feedback with suggestions about their learning strategies. By providing frequent suggestions about specific tasks in the course, students will be able to quickly adjust their learning progressively.
The agenda for the session is as follows:
The case for using data to provide personalized feedback to students (45 minutes).
Scenario 1: Providing feedback on automatically graded assessments (45 minutes).
Scenario 2: Encouraging student engagement in real time (60 minutes).
Scenario 3: Using predictive models to suggest study strategies (45 minutes).
Aberlardo Pardo is Associate Professor and Associate Head of Teaching and Learning at the School of Electrical and Information Engineering, The University of Sydney. He has a PhD in Computer Science by the University of Colorado at Boulder. He is the director of the Learning and Affect Technologies Engineering (LATTE) laboratory specialized in educational technology, and co-director of the Learning Analytics Research Group. His areas of research are learning analytics, software for collaborative and personalized learning, and technology to improve the student experience and teaching practice. He is also research fellow at the LINK Research Lab (The University of Texas at Arlington), co-director of the Education Innovation Unit of the Faculty of Engineering and IT (The University of Sydney), and Vice-President of the Society for Learning Analytics Research (SoLAR).